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• W2805337002 abstract "The existing strategies in PRF (Pseudo relevance feedback) have insufficient attention to the user real query intention and fail to model the user intricate activities, which are plagued by the feedback terms sensitive problem. The major challenge in PRF lies in how to get the reliability relevant content for the user query. For this purpose, we propose a novel PRF approach by diversifying the feedback documents. An abstract pseudo document is proposed to represent the content of each feedback document so as to cover as diverse aspects of the feedback set as possible. The experimental results on real data sets show that our method can obtain the better feedback source to improve the overall the performance of PRF." @default.
• W2805337002 created "2018-06-13" @default.
• W2805337002 creator A5033563552 @default.
• W2805337002 creator A5076174513 @default.
• W2805337002 date "2017-11-01" @default.
• W2805337002 modified "2023-09-24" @default.
• W2805337002 title "Pseudo-Based Relevance Analysis for Information Retrieval" @default.
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• W2805337002 doi "https://doi.org/10.1109/ictai.2017.00190" @default.
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